Vetted GPT Professionals

“Built and deployed a production LLM-powered RAG knowledge system to unify operational/policy information across PDFs, wikis, and databases, emphasizing auditability and low-latency/cost performance. Improved answer relevance at scale by moving from pure vector search to hybrid retrieval with metadata filtering and reranking, and partnered closely with healthcare operations/compliance to define acceptance criteria and human-in-the-loop guardrails.”

“Built and deployed a production NLP sentiment analysis system at Piper Sandler to turn noisy, finance-specific customer feedback into scalable insights. Demonstrates strong end-to-end MLOps: fine-tuning BERT, improving label quality, monitoring for language drift, and automating retraining/deployment with Airflow and Docker (plus Kubeflow exposure).”

Tarun Gowda

Screened

Mid-level AI Engineer specializing in Generative AI and multimodal RAG

Morristown, NJ3y exp

“Full-stack engineer who helped build and launch an internal genAI platform called GAIL, supporting multiple LLMs, confidential document upload for RAG pipelines, and collaborative chat. Worked across FastAPI, React/TypeScript, AWS/DynamoDB, and Azure, with notable ownership of backend RAG logic, MCP integration architecture, and frontend fixes that improved chat usability.”

“Robotics software engineer focused on backend/integration for indoor autonomous mobile robots, with hands-on ROS 2 experience integrating Nav2/AMCL/TF2 and LiDAR/camera pipelines. Emphasizes production readiness—robust failure recovery, QoS-tuned distributed communication, and strong observability (logging/health checks)—validated through Gazebo simulation, sensor-data replay debugging, and Docker-based CI/CD deployment.”

“Backend/ML engineer who has built both enterprise data pipelines and real-time AI products: modular Python (Flask/FastAPI) services integrating automation scripts and low-latency ML inference (MediaPipe, PyTorch) plus OpenAI-powered feedback. Demonstrated measurable performance wins (~30% faster HR workflows; ~40% faster AWS pipelines across 100+ Oscar Health feeds) and strong multi-tenant/data-isolation patterns (schema-based isolation, RBAC, microservices).”

“Data science/AI intern at University at Buffalo Business Services who built and deployed production systems spanning classic ML and LLM assistants. Delivered real-time competitor intelligence for a Cornell-partnered, $1B beverage launch by scraping/cleaning 5,000+ SKUs and deploying models via API, then built a domain-aware LLM assistant to modernize Excel-based workflows with strong grounding, privacy controls, and sub-5s latency.”

Mark Kevin Macadangdang

Screened ReferencesStrong rec.

Senior Solutions Architect specializing in enterprise integrations for PropTech and facilities management

Peterborough, Canada12y exp

“Facilities-management-focused functional consultant/business analyst with nearly a decade in CAFM/CMMS-aligned implementations since 2016, centered on MRI/FSI Evolution. Brings unusually strong depth across workflow automation, cross-system integrations, and finance/compliance-heavy operational processes, including SOR-based claims, contractor compliance, and RCTI invoicing.”

“Education product leader who has built and rebuilt large-scale learning platforms from the ground up, including GamED Academy, a Minecraft-based K-12 platform that grew to 6,000+ active students per cycle and produced IP tied to a Microsoft/Mojang exit. Particularly compelling for mission-driven edtech roles: combines curriculum, UX, platform, and AI product leadership with a clear philosophy that technology should amplify human teaching relationships rather than replace them.”

“AI/ML engineer with production experience building an enterprise network-fault prediction assistant that combines anomaly detection (Isolation Forest + LSTM) with an LLM layer for incident diagnosis and recommended resolutions. Hands-on with orchestration (Airflow, Prefect, Dagster) to run ETL/ELT and automated training/fine-tuning workflows, and has delivered AI solutions with non-technical stakeholders (retail customer support ticket categorization/response suggestions).”

“End-to-end engineer focused on AI-native enterprise systems, including a production generative knowledge platform using RAG + semantic search over internal documentation (React, Python/Flask, GPU-hosted NLP models, Pinecone) with strong CI/CD and observability. Reports concrete outcomes including 40% faster knowledge access and ~75% employee adoption, and has led incremental cloud-native modernization using feature flags, parallel runs, canary releases, and regression testing.”

Isha Harne

Screened

Intern Software Engineer specializing in ML applications and LLM platform engineering

New York, NY1y exp

“Full-stack engineer who builds and scales customer-facing and internal AI products end-to-end (React/TypeScript/FastAPI/MongoDB) with strong product instrumentation and rapid MVP iteration. Built an AI-powered code review assistant adopted across teams and integrated into CI/CD, reducing manual review time by 30%+, and has hands-on experience with LLM retrieval/reasoning systems (LangChain + FAISS) and microservices scaling using RabbitMQ, Docker, and AWS.”

“AI/ML engineer with hands-on experience building a healthcare-focused generative AI application end-to-end, from architecture and data design through deployment, monitoring, and iterative improvement. Particularly strong in multi-agent LLM systems, fine-tuning, and safety guardrails, with measurable impact including a 20% accuracy lift to 91% and 10% latency improvement in a nutrition recommendation pipeline.”

“Master’s program project lead who built and deployed a real-time sound recognition system (Flask + React Native + ML) that was adopted by 200+ university students. Demonstrates strong production engineering and cross-layer debugging—solving latency, unreliable uploads, and observability gaps using microservice separation, chunked/idempotent transfers, and packet-capture-driven network diagnosis—plus AWS/on-prem and IoT edge-to-cloud integration experience.”

“Accenture engineer who built and shipped a production RAG-based automation/chatbot for SAP incident triage and troubleshooting, embedding thousands of runbooks/logs/tickets into a semantic search pipeline and integrating it into Teams/Slack. Reported major productivity gains (30–60% time reduction), >90% validated answer accuracy, and sub-2-second responses, with strong orchestration (Airflow/Prefect/LangGraph) and reliability practices (guardrails, testing, monitoring).”

“LLM/RAG-focused engineer who repeatedly takes agentic workflows from impressive demos to dependable production using rigorous evals, SLOs, and deep observability. Has led high-impact incident mitigation (22-minute MTTR during a major sale) and developer enablement workshops, and partnered with sales to close a $410k ARR enterprise deal with a tailored RAG pilot (FastAPI/pgvector/Okta/InfoSec-ready).”